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AB Test Setup

v1.0.0

Plan A/B tests with a clear hypothesis, defined metrics, variant design, sample size, duration, and statistical significance guidelines.

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by @amdf01-debug·MIT-0
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License
MIT-0
最后更新
2026/3/22
安全扫描
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Pending
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OpenClaw
安全
high confidence
This is an instruction-only skill that provides a coherent, self-contained checklist and output template for planning A/B tests and does not request extra permissions, credentials, or perform any installs.
评估建议
This skill is an offline planning template for A/B tests and appears self-contained. You can install it safely from an access/permission perspective, but remember: (1) provide accurate traffic and baseline numbers when using the template so sample-size estimates are meaningful; (2) avoid including any personally identifiable information (PII) in examples you pass to the agent; and (3) the agent can invoke the skill autonomously by default — if you prefer manual control, disable autonomous invoca...
详细分析 ▾
用途与能力
The name and description (A/B test planning) match the SKILL.md: it provides hypothesis, metrics, sample size, duration, design, and decision rules. The skill does not request unrelated binaries, env vars, or credentials.
指令范围
Runtime instructions are limited to planning steps, output formatting, and rules for running/deciding on tests. They do not instruct the agent to read local files, access environment variables, call external endpoints, or perform system operations.
安装机制
No install spec and no code files — the skill is instruction-only, so nothing is written to disk or installed during setup.
凭证需求
The skill declares no environment variables, credentials, or config paths and the instructions do not reference any secrets or external service tokens.
持久化与权限
always is false (no forced inclusion). disable-model-invocation is default false (agent may call the skill autonomously), which is normal for skills and acceptable here since the skill has no sensitive access.
安全有层次,运行前请审查代码。

License

MIT-0

可自由使用、修改和再分发,无需署名。

运行时依赖

无特殊依赖

版本

latestv1.0.02026/3/22

Initial release

● Pending

安装命令 点击复制

官方npx clawhub@latest install sw-ab-test-setup
镜像加速npx clawhub@latest install sw-ab-test-setup --registry https://cn.clawhub-mirror.com

技能文档

Trigger

Plan A/B tests with proper methodology — hypothesis, sample size, duration, variant design, statistical significance.

Trigger phrases: "A/B test", "split test", "experiment", "test this change", "variant", "multivariate test", "hypothesis"

Process

  • Hypothesis: What are you testing and why?
  • Metrics: Primary metric, guardrail metrics, success criteria
  • Design: Control vs variant(s), what exactly changes
  • Calculate: Sample size, test duration, minimum detectable effect
  • Plan: Implementation, QA, analysis timeline

Output Format

# A/B Test Plan: [Name]

Hypothesis

If we [change], then [metric] will [improve/increase] because [reason].

Variants

  • Control (A): [current experience]
  • Variant (B): [proposed change — be specific]

Metrics

  • Primary: [metric] — current: [X%] — target: [Y%]
  • Guardrail: [metric that should NOT decrease]

Sample Size & Duration

  • MDE: [minimum detectable effect, e.g., 10% relative]
  • Sample needed: [N per variant]
  • Current traffic: [X visitors/day to test area]
  • Estimated duration: [Y days/weeks]
  • Confidence level: 95%

Implementation Notes

[What needs to change, where, any technical considerations]

Decision Framework

  • If primary metric improves ≥ MDE with p < 0.05 → ship variant
  • If no significant difference after [duration] → keep control
  • If guardrail metric drops > [threshold] → stop test immediately

Rules

  • Never run a test without a hypothesis
  • One change per test (unless multivariate with sufficient traffic)
  • Run for minimum 2 full business cycles (usually 2 weeks)
  • Don't peek at results daily — pre-commit to evaluation date
  • 95% confidence minimum. 80% power minimum.
  • Document everything: future you needs to know why this was tested
数据来源:ClawHub ↗ · 中文优化:龙虾技能库
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